ASAM OpenLABEL

ASAM OpenLABEL

DATASHEET
Title
Annotation format and methods for multi-sensor data labeling and scenario tagging
Domain
Simulation
Current Version
1.0.0
Release Date
12 Nov 2021
Application Areas
  • Multi-sensor data labeling
  • Scenario tagging
Specification Content
  • JSON schema for multi-sensor data labeling and scenario tagging
  • Standardized set of tags for scenario tagging
  • File format specification
File Formats
  • json

ASAM OpenLABEL defines the annotation format and labeling methods for objects and scenarios. ASAM OpenLABEL provides a guideline on how the labeling methods and definitions should be used.

From working with different customers, a significant fragmentation emerged in the way each individual organization categorizes and describes the objects populating the driving environment. Such categorizations and descriptions are the fundamental building block of any Autonomous Driving System’s (ADS) perception stack, since it is through them that an ADS comes to a basic and profound understanding of the status of around its surrounding.

The lack of a common labeling standard in the industry is the root cause of several different issues:

  • Hampered Vehicle2Vehicle Interaction: The different descriptions and understandings of surroundings may cause casualties in complex situations involving two or more different ADSs
  • Precluded sharing: It is a highly difficult if not impossible task to share data across organizations that adopted different labeling taxonomies and specifications
  • Reduced annotation quality: Each individual labeling task requires ad-hoc training and even development of custom software functions that translate into a higher probability of errors and thus a threat to safety
  • Deprecation of old labels: Long-term operation of ADS development imply changes in quantity and comprehensiveness of labels to be produced considering the evolution of the driving scenes, new sensors, and scenarios. As a consequence, a flexible descriptive language is required to absorb future extensions and modifications of labels and guarantee backward-compatibility.


JSON Format

The use of a standardized format will help save cost and resources in converting annotated data. ASAM OpenLABEL will be represented in a JSON format and can therefore be easily parsed by tools and applications. ASAM OpenLABEL will specify which coordinate systems are used as reference for the label. This already facilitates the conversion a lot.
 

Extended Labeling Objects

ASAM OpenLABEL will also provide methods to label objects in a scene (one point in time/ frame) as well as across multiple scenes by enhancing the methods to label actions, intentions and relations between objects.
 

Labeling Different Data Types

The ASAM OpenLABEL format will be capable of managing different types of labeling methods, for different types of data. This includes 2D and 3D bounding boxes, the rotation of 3D bounding boxes, semantic segmentation of images and point clouds. These semantic segmentations can be either instance classes, single/multi-class, partial or full classes.
 

It is important that the labeling fits into the taxonomy definitions of a user/company. For that reason, the project group intends to provide ASAM OpenLABEL with the ability to import ontologies and taxonomies for the labeling process. The ASAM OpenLABEL project group is closely interacting with the ASAM OpenXOntology project to align ASAM OpenLABEL with the OpenX domain model and to provide requirements for the ASAM OpenXOntology standard. As ASAM OpenLABEL and ASAM OpenXOntology are currently being developed in parallel, the ASAM OpenLABEL standard will be developed with an external ontology. The experience on using ASAM OpenLABEL with different ontologies can be used to give the user a guideline on how to import their own ontology and use this with ASAM OpenLABEL. It might be possible that the use of foreign ontologies will require a certain standardized ontology format.

 

Standard Authors

Advanced Data Controls Corp.,   Annotell AB,   Ansys Inc.,   Deepen AI,   Deutsches Zentrum für Luft- und Raumfahrt e. V.,   Five,   iASYS Technology Solutions Pvt. Ltd.,   LiangDao GmbH,   Peak Solution GmbH,   SAIC Motor Corporation Ltd.,   Tata Consultancy Services Pvt. Ltd,   understandAI GmbH,   Vicomtech,   WMG University of Warwick
 


DATASHEET
Title
Annotation format and methods for multi-sensor data labeling and scenario tagging
Domain
Simulation
Current Version
1.0.0
Release Date
12 Nov 2021
Application Areas
  • Multi-sensor data labeling
  • Scenario tagging
Specification Content
  • JSON schema for multi-sensor data labeling and scenario tagging
  • Standardized set of tags for scenario tagging
  • File format specification
File Formats
  • json
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